Upon reading the chapter “What makes bad figures bad?” by Kieran Healy there were three key takeaways that he asserts are the primary categories where visualizations can get into the weeds when presenting data narratives: aesthetics - which he describes as “ugly or inconsistent design choices”, substantive - problems that arise due to the way the data is being presented, and perceptual - visualizations that are confusing or misleading due to how people perceive and process what they see.
Healy suggests that there are certain design principles that should be utilized to mitigate these flaws, which include choosing the visualizations that are most appropriate for the data to be presented, maximizing the “data to ink” ratio, and centering simplicity over creating eye-catching visuals that can detract from the data itself. Healy also warns us how easy it is to unintentionally (and intentionally) place value judgements on the presented data by the visual choices we make and cherry picking information that we want to present. I certainly can’t argue with this.
I do however, have one point of contention: I agree with Healy that bad taste is in fact bad, but what one may consider “ugly” is entirely subjective and should be considered separately from the aesthetics that mislead the consumers of the data. I certainly hate the Comic Sans font as much as the next design snob, but that shouldn’t give me license to write off the legitimacy of the content. Unfortunately this is a human trait - to diminish the quality of the content in favor of what is simply easy on the eyes. It may not be fair, but in this regard it is a responsibility of data visualizers to make good data, look good too.